Market Research For Harvest Kitchen

Problem statement

New entrepreneurs undergo myriad of challenges before establishing themselves in the market. So to remain afloat in the market, a well laid strategy is important (Kotler 2012) and (Romano, 2009). However, the strategy will be responsive to the business’ challenges if it’s all based on customer feedback and performance of their products in the market. The challenges include competition from well established businesses within the industry to aspects such as weather and unfavorable economy (Mowlana & Smith, 2003) and (March, 2009). For this reason, it is therefore important that businesses identify their strong points and capitalize on them and their weak points to improve on them.

Harvest kitchen is a generally new organization dealing in fruits and vegetables. Just like any other new ventures, they have had to deal with challenges facing new businesses in the market and within its industry. The main problems identified so far by Harvest kitchen organization is low sales. It is perceived that this is as a result of low closes on leads. The other problem is cost of goods. Sometimes due to unfavorable market conditions their fruit and vegetables are only able to fetch small prices hence small profit margins. It is against this background that the management of Harvest Kitchen decided to conduct a survey on their organization to identify their weak points so as to strategize on how to turn the situation around.

Figure 1

An analysis of sales performance for various products at Harvest Kitchen is as shown in the graph above. The graph shows that the best performing product in terms of sales in the business was drinks. As can be observed, drinks fetched for the organization 20673.7 thousand dollars in a year. This was highest compared to the other products. The other business within Harvest that performed relatively well was the bakery business. It fetched the organization an amount equivalent to 10,137.55 thousand dollars in a year. The worst performing products in terms of sales was chocolate slices. The business was only able to get 135.92 thousand dollars while also getting low sales in Ayuderic which earned the businesses 678.75 thousand dollars.

Testing whether there is a significant difference in payment techniques (Visa and Credit)

In business transactions such as buying and selling, there are various methods of making payments. Payment can be made through cash, credit, money gram, visa and MasterCard. The diversified methods of payment make it convenient for various customers from different geographical regions to be able to make payments with ease. In regard to this, Harvest Kitchen has diversified its payment methods so as to be able to accommodate more customers without locking out potential customers due to their available methods of payment. The business provides two main methods of payments; credit and visa. A number of their payments have been done through visa as well as credit. To establish whether there was a significant difference between the two methods of payments. A paired sample t-test was used to establish the existence of the significant difference. The test hypothesis was as below;

Graph showing level of sales for different products

Hypothesis

At 0.05 level of significance,

H₀: There is no significant difference between payments through credit and visa.

 Versus

H₁: There is significant difference between payments through credit and visa.

The test results were as below

Paired Samples Statistics
	Mean	N	Std. Deviation	Std. Error Mean
Pair 1	credit	584.8115	366	228.86716	11.96308
visa	555.8443	366	244.88987	12.80060

Table 2

Paired Samples Correlations
	N	Correlation	Sig.
Pair 1	credit & visa	366	.931	.000

Table 3

Paired Samples Test
	Paired Differences	t	df	Sig. (2-tailed)
Mean	Std. Deviation	Std. Error Mean	95% Confidence Interval of the Difference
Lower	Upper
Pair 1	credit – visa	28.96721	89.48246	4.67732	19.76933	38.16510	6.193	365	.000

Table 4

Table 2, 3 and 4 shows the results of the test for the difference in payment methods. Table 2 shows a correlation coefficient of .93. This is a strong correlation since it is almost equal to 1. To add on the correlation is in the positive direction. This means that there is a strong relationship between the two variables or methods of payments. The t-test from table 4 indicates a p-value of .00. If this value is compared to the level of significance which is .05, then we are directed to fail to accept the null hypothesis and accept the alternative. The conclusion is that there is significant difference between payments through credit and visa.

Does location of items in a shop influence their buying?

Research has shown that arrangement of items in a shop can really influence the amount of the product being bought. For example, products that are conspicuously displayed at the entrance will attract more customers than those that have been displayed at the shelves in the backend of the shop. To add on, items put together or close to fast moving consumer goods are likely to be bought more due to the influence of the fast moving goods. The idea here is the impulse effect that they induce on shoppers who tend to buy just because they have seen the product and not because they needed the product. Harvest Kitchen has 5 locations. They wanted to establish whether the 5 locations influenced sales in any way. For this reason an analysis of variance was conducted to establish whether there was a mean difference in the sales amounts for the locations. An analysis of variance was found appropriate since the variables under test are more than two (5 locations).

The test hypothesis

H₀: There is no significant difference in the sales of products from the five locations.  

Versus

H₁: At least sales from one location are different.

ANOVA
	Sum of Squares	df	Mean Square	F	Sig.
location1	Between Groups	295770218.364	185	1598757.937	4.448	.000
Within Groups	44931115.379	125	359448.923
Total	340701333.743	310
location2	Between Groups	51535594.667	185	278570.782	2.754	.000
Within Groups	12643881.982	125	101151.056
Total	64179476.650	310
location3	Between Groups	244998995.000	11	22272635.909	.	.
Within Groups	.000	0	.
Total	244998995.000	11
location4	Between Groups	177884531.450	147	1210098.853	1.389	.139
Within Groups	27873309.500	32	871040.922
Total	205757840.950	179

Table 5

An analysis of variance is used to establish whether there is a difference in means of more than two variables. For decision on hypothesis to be reached, the p-value computed is compared to the value of the level of significance. If the p-value is less than the level of significance then the null hypothesis is rejected and the alternative accepted. From table 5 above, it can be observed that the p-values (.00) are generally less than the level of significance which is .05. This therefore means that the null hypothesis is not accepted. The conclusion made is therefore that the sales from one location are different. In order to determine the location or locations whose sales are significantly different, then further tests such as Duncan’s tests are recommended.

Are sales and gross profit different among the months?

Significant difference in payment techniques

Among the factors that normally affect sales are seasons. There are economic seasons and climate. Favorable climate increases production especially in when it comes to agricultural products. Bad climate on the other hand leads to low production of agricultural products. Harvest Kitchen grows fruits and vegetables which are also affected by weather across the year. This means that their production and hence will always vary sales. What is not evident is the extent to which these factors affect the sales and hence the gross profits. To establish whether there is any difference in sales and gross profits in the months of the year due to various factors, an analysis of variance was employed to establish the same.  

The test hypothesis is as below;

H₀: The mean sales level is generally the same across all the months of the year.

Versus

H₁: At least one month is different in terms of sales.  

The test’s confidence level is 95%

ANOVA
	Sum of Squares	df	Mean Square	F	Sig.
Net sales January	Between Groups	3678107.097	30	122603.570	.	.
Within Groups	.000	0	.
Total	3678107.097	30
Net sales February	Between Groups	1492938.000	28	53319.214	.	.
Within Groups	.000	0	.
Total	1492938.000	28
Net sales March	Between Groups	4187028.774	30	139567.626	.	.
Within Groups	.000	0	.
Total	4187028.774	30
Net sales April	Between Groups	2786878.800	29	96099.269	.	.
Within Groups	.000	0	.
Total	2786878.800	29
Net sales May	Between Groups	3317298.839	30	110576.628	.	.
Within Groups	.000	0	.
Total	3317298.839	30
Net sales June	Between Groups	1418345.467	29	48908.464	.	.
Within Groups	.000	0	.
Total	1418345.467	29
Net sales July	Between Groups	1765256.194	30	58841.873	.	.
Within Groups	.000	0	.
Total	1765256.194	30
Net sales Aug.	Between Groups	2698581.935	30	89952.731	.	.
Within Groups	.000	0	.
Total	2698581.935	30
Net sales Sep	Between Groups	2248828.000	29	77545.793	.	.
Within Groups	.000	0	.
Total	2248828.000	29
Net sales Oct	Between Groups	3395575.419	30	113185.847	.	.
Within Groups	.000	0	.
Total	3395575.419	30
Net sales Nov	Between Groups	2655303.367	29	91562.185	.	.
Within Groups	.000	0	.
Total	2655303.367	29

Table 6

The table above shows the results of the analysis of variance. From the p-values computed above (0.00), it can be seen that they are less than the level of significance which is .05. The ANOVA test directs that if the p-value is less than the level of significance then the null hypothesis is rejected and the alternative accepted. The converse is also true. Since the p-value are less than the level of significance in general (.00 <.05), we fail to accept the null hypothesis and therefore fail to reject the alternative. It is therefore concluded at least one month is different in terms of sales.  

Hypothesis

H₀: There is no significant difference in gross profit across the 12 months of the year.

Versus

H₁: At least one month is different in terms of gross profit.  

In this hypothesis, 95% confidence level has been applied.

The ANOVA results are tabulated as below,

ANOVA
	Sum of Squares	df	Mean Square	F	Sig.
jan_gp	Between Groups	50905.200	28	1818.043	4.474	.199
Within Groups	812.659	2	406.329
Total	51717.859	30
feb_gp	Between Groups	7919.956	27	293.332	.164	.980
Within Groups	1791.610	1	1791.610
Total	9711.566	28
march_gp	Between Groups	7419.298	28	264.975	1.097	.586
Within Groups	482.904	2	241.452
Total	7902.202	30
Apr gp	Between Groups	3216.693	27	119.137	.156	.995
Within Groups	1528.049	2	764.024
Total	4744.742	29
may_gp	Between Groups	12456.400	28	444.871	36.134	.027
Within Groups	24.623	2	12.312
Total	12481.023	30
june_gp	Between Groups	6554.626	27	242.764	25.033	.039
Within Groups	19.395	2	9.698
Total	6574.022	29
july_gp	Between Groups	8586.214	28	306.651	2.287	.350
Within Groups	268.174	2	134.087
Total	8854.388	30
aug_gp	Between Groups	12119.709	28	432.847	1.136	.574
Within Groups	762.284	2	381.142
Total	12881.994	30
sept_gp	Between Groups	22640.467	27	838.536	.116	.999
Within Groups	14424.341	2	7212.171
Total	37064.809	29
oct_gp	Between Groups	42678.595	28	1524.236	1.087	.590
Within Groups	2803.541	2	1401.771
Total	45482.136	30
nov_gp	Between Groups	68636.751	27	2542.102	2.032	.383
Within Groups	2502.487	2	1251.244
Total	71139.239	29

Table 7

The table above shows the results of the analysis of variance. From the p-values computed above (0.00), it can be seen that they are less than the level of significance which is .05. The ANOVA test directs that if the p-value is less than the level of significance then the null hypothesis is rejected and the alternative accepted. The converse is also true. Since the p-value are less than the level of significance in general (.38 >.05), we fail to reject the null hypothesis and therefore fail to accept the alternative. The conclusion therefore is that there is no significant difference in gross profit across the 12 months of the year.

Is There Association Between Sales And Rainfall?

In order to establish whether there was a relationship between sales and rainfall, the research study employed the use of Pearson correlation. In this test the correlation coefficient is used to determine the extent of relationship. A perfect positive correlation has a correlation coefficient of 1 while a perfect negative correlation has a correlation coefficient of -1.

Correlations
	SALES	RAINFALL
SALES	Pearson Correlation	1	.057
Sig. (2-tailed)		.273
N	366	366
RAINFALL	Pearson Correlation	.057	1
Sig. (2-tailed)	.273
N	366	366

Table 8

It can be observed that the correlation coefficient is .06. This is an indication that the correlation between the two variables is not that strong though it is positive.

Tests For Correlation Between Sales Performance And Profits

In order to establish whether there was a relationship between sales and rainfall, the research study employed the use of Pearson correlation. In this test the correlation coefficient is used to determine the extent of relationship. A perfect positive correlation has a correlation coefficient of 1 while a perfect negative correlation has a correlation coefficient of -1.

Correlations
	SALES	Net profit
SALES	Pearson Correlation	1	.017
Sig. (2-tailed)		.745
N	366	366
Net profit	Pearson Correlation	.017	1
Sig. (2-tailed)	.745
N	366	1034

Table 9

It can be observed that the correlation coefficient is .02. This is an indication that the correlation between the two variables is not that strong though it is positive.

Graph of monthly gross profit.

Figure 2

The graph above shows that the gross profits realized by Harvest Kitchen across the 12 months are almost normally distributed. It can also be observed that there was no big variance in the amounts of gross profit in the months of the year.

From the analysis of the Harvest Kitchen business, there were no relationship found between rainfall and the amounts of sales that were made for the whole year. This therefore indicates that the business should not rely on rainfall when projecting about their sales. To add on, it was found that the gross profits across the months were generally equal, that is there was no significant difference in the gross profits across the months. The research report therefore recommends that the business embark on a robust sales and marketing strategies to increase sales and hence gross profits from month to month.

References

Kotler , P. (2012). Marketing Management: Analysis, Planning, Implementation and Control,. Englewood Cliffs, NJ.: Prentice-Hall.

March, R. (2009). Tourism marketing myopia”, Tourism Management. (Vol. 15).

Mowlana, H., & Smith, G. (2003). Marketing in a global context: the case of frequent traveler programs. Journal of Travel Research, 33, 20-27.

Romano, C. (2009). Research strategies for small business: a case study. International Small Business Journal, 7, 35-43.